Hiroki Yanagisawa, Kohei Miyaguchi, et al.
NeurIPS 2022
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
Hiroki Yanagisawa, Kohei Miyaguchi, et al.
NeurIPS 2022
Shashanka Ubaru, Sanjeeb Dash, et al.
NeurIPS 2020
Alice Driessen, Susane Unger, et al.
ISMB 2023
Oliver Bodemer
IBM J. Res. Dev